In this part of the course, we will cover the following concepts:
| Objective | Complete |
|---|---|
| Describe classification and its uses | |
| Summarize the steps and application of kNN |
Classification is a type of supervised learning method
This translates into having two types of variables in our data:
Depending how many categories are within the target variable, we will have
| Classification | Regression | |
|---|---|---|
| Target variable | Discrete, usually binary | Continuous |
| Types | Binary, Multi-Class | Linear, polynomial |
| Algorithms | Decision trees, random forests, logistic regression, k-Nearest Neighbors | Linear regression, regression trees, time-series regression |
| Question | Example |
|---|---|
| What is this object like? | Selecting similar drugs or similar diseases |
| Who is this person like? | Finding patients that are suffering similar symptoms |
| What category is this in? | Anticipating if your patient will need emergency services |
| What is the probability that something is in a given category? | Determining the probability that a drug is a particular type or can be used for a particular treatment |
| Objective | Complete |
|---|---|
| Describe classification and its uses |
✔ |
| Summarize the steps and application of kNN |
| Objective | Complete |
|---|---|
| Describe classification and its uses |
✔ |
| Summarize the steps and application of kNN |
✔ |